Teaching

Overview

I received formal pedagogical training at Michigan Technological University through courses such as MA 5901: Teaching College Mathematics and MA 5904: Teaching Online Courses, which provided a strong foundation in evidence-based teaching methods, course design, and digital instructional tools. I have also participated in several teaching conferences and workshops that deepened my understanding of inclusive pedagogy, equitable assessment practices (including alternative grading and transparent assignment design), and accessible course design. I continue to refine my practice through active-learning strategies, flipped-classroom approaches, support for diverse learners, and the thoughtful use of learning analytics to improve engagement and outcomes. These experiences directly shape my day-to-day teaching and inform the way I mentor Preceptors/GTAs, guide seniors preparing their capstone presentations, and contribute to departmental initiatives.

Teaching Experience

Full-time Lecturer

Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University
Fall 2026–Present

Teaching STAT 312: Basic Statistics for Engineering and Science, with an emphasis on statistical reasoning, data analysis, and applications relevant to engineering and scientific disciplines.
Developing and maintaining comprehensive course materials and an interactive course website using Quarto, GitHub, and R to provide students with organized, reproducible, and accessible learning resources.
Designing lectures, in-class activities, assignments, supplemental practice materials, and computational exercises to connect statistical concepts with real-world applications.
Planning to Contribute to student mentoring, including Capstone Projects, curriculum development, and the training and mentoring of teaching assistants (TAs).

Visiting Assistant Professor

Department of Mathematics, Statistics, and Computer Science, Macalester College
Fall 2025-Summer 2026

Teaching courses at both the foundational and higher levels of the undergraduate statistics and data science curriculum.
Designing and continually updating the course website using Quarto, integrated with GitHub for version control and R for reproducible workflows, ensuring students have seamless access to current materials and activities.
Enhancing course curriculum in collaboration with other instructors to align learning outcomes with program objectives and integrating statistical software to strengthen students’ analytic fluency.
Mentoring undergraduate researchers from project conception through analysis and conference-style presentations; preparing students to present their work at research symposia.

Graduate Teaching Instructor

Department of Mathematical Sciences, Michigan Technological University
Spring 2020–Spring 2025
Course: Engineering Statistics (taught across in-person, hybrid, synchronous online, and asynchronous online formats)

Instructed MA 3710: Engineering Statistics across multiple modalities: in-person, hybrid, synchronous online, and asynchronous online.
Maintained average student evaluation scores of 4.21 +/ 5.00 in 14 of 15 semesters (~ 93% of courses). Notably, 4.21 matches the overall average student evaluation score of MTU faculty over the past seven years.
Designed and continually updated course materials, incorporating semester-long projects using statistical software to develop both theoretical insight and hands-on analytic skills.
Produced a full suite of instructional assets—short lecture videos, annotated slide decks, and concise handouts- to support flexible, asynchronous learning
Designed, administered, and graded every homework set, quiz, project, and examination, ensuring alignment with departmental rubrics
Supervised undergraduates transforming class project ideas into posters for the annual Undergraduate Research & Scholarship Symposium and advised former students on research-methodology questions for the Graduate Research Colloquium, helping them integrate statistical reasoning into honors theses.

Graduate Teaching Instructor

Department of Applied Statistics and Research Methods, University of Northern Colorado
Fall 2018–Summer 2019
Course: Introduction to Statistics (face-to-face general-education course)

Collaborated with instructors of record to redesign syllabi, examples, and assignments during a department-wide textbook transition.
Administered and graded homework, quizzes, projects, and examinations; maintained detailed analytics to track student progress
Mentored undergraduates preparing posters for the Research Evening and McNair Scholars Program, guiding study design and data analysis.

Graduate Teaching Assistant

Dr. Kui Zhang, Department of Mathematical Sciences, Michigan Technological University
Fall 2022–Spring 2023
Courses: Design & Analysis of Experiments, Applied Generalized Linear Models

Supported instruction in advanced undergraduate and graduate statistics courses
Held office hours and provided comprehensive assistance—from theoretical proof explanations to hands-on statistical-software implementation
Graded all homework and quizzes, supplying detailed, formative feedback to enhance student mastery

Beth Reed, Department of Mathematical Sciences, Michigan Technological University
Fall 2019
Courses: Statistical Methods

Co-instructed selected lectures in Statistical Methods (a university-core course) and provided ongoing classroom support
Conducted office hours, guiding students from theoretical concepts to practical applications relevant to their research
Held office hours, providing supports from theoretical to application to the students so that they can see relevancy of statistical methods in their area of research
Graded homework, quizzes, and exams, furnishing individualized feedback to promote deeper understanding

Courses

Course	Institution	Sections Taught	Semesters Taught	Topics and Duties	Syllabus	Materials
Introduction to Statistical Modeling	Macalester College	2	Sp26 (x2), Fa25 (x2)	Students learn exploratory data analysis and visualization; Simple and multiple linear regression with comprehensive model building–addressing confounding, interaction effects, variable selection, diagnostics, and transformations; Simple and multiple logistic regression with interpretation of odds ratios and evaluation of classification performance; Core inference for modeling (confidence intervals, hypothesis testing, F-tests); Semester-long research projects	Spring26 Syllabus, Fall25 Syllabus	Course Website-Sp26, Course Website-Fall25
Statistical Machine Learning	Macalester College	1	Sp26	Topics include model-evaluation strategies (overfitting diagnostics, cross-validation); regression model-selection techniques (LASSO and non-parametric alternatives); flexible models (K-NN with bias–variance considerations, LOESS, GAM); classification methods (logistic regression, K-NN, decision trees) and their performance metrics; ensemble learning (bagging, random forests); unsupervised learning (hierarchical and k-means clustering); and dimensionality-reduction tools such as principal component analysis (PCA) and principal component regression; Semester-long research projects	Spring26 Syllabus	Course Website-Sp26
Engineering Statistics (GTI)	MTU	15	Fa20,21,23,24, Sp20–25, Su20–24	Independently instructed this foundational course, covering: exploratory data analysis and visualization; Probability theory and Bayes’ theorem; Discrete and continuous distributions; Central Limit Theorem; Statistical inference including confidence intervals and hypothesis testing for means, proportions, and variances (t-tests, F-tests, Chi-square tests); Statistical process control and control charts; Linear regression modeling (simple, multiple, diagnostics, ANOVA); Principles of experimental design	Sp25 Syllabus	Live-Notes, Handouts, Class-Notes, Practice Problem
Applied Generalized Linear Models (GTA)	MTU	1	Sp23	Assisted as TA: held office hours, moderated online discussions panel, graded assignments, and provided consultation on course topics and projects in Statistical modeling foundations; Linear regression and model diagnostics; Maximum likelihood estimation; Generalized linear models (GLMs) including structure, estimation, inference, and diagnostics; Models for proportions (binomial GLMs), counts (Poisson and negative binomial GLMs), and positive continuous data (gamma and inverse Gaussian GLMs); Tweedie models; Nominal and ordinal logistic regression		Class-Notes
Design & Analysis of Experiment (GTA)	MTU	1	Fa22	Assisted as TA: held office hours, moderated online discussions panel, graded assignments, and provided consultation on course projects and topics in Principles of experimental design; ANOVA for one-way, factorial, and randomized complete block designs; post-hoc analysis, including contrasts and multiple comparison procedures; model diagnostics, assumption checking, and data transformations; and ANCOVA, including power and sample size determination		Class-Notes
Statistical Methods (GTA)	MTU	1	Fa19	Assisted as TA: co-taught coursework; held office hours, graded assignments and provided consultation on course topics in descriptive statistics and data visualization; fundamental probability theory; discrete and continuous distributions, including the binomial and normal; confidence-interval construction and hypothesis testing for one- and two-sample means; simple linear regression modeling and prediction; and one- and two-way ANOVA		Handouts
Introduction to Statistical Analysis (GTI)	UNCo	3	Fa18, Sp19, Su19	Instructed core statistical concepts including data organization (frequency distributions, histograms), measures of central tendency and variability, probability distributions, estimation, hypothesis testing, regression, and ANOVA. Actively contributed to ongoing course development, collaborated with fellow instructors to enhance course textbook and materials